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Updated: Dec 28, 2025

Antigenic Liposomes for Generation of Disease-specific Antibodies

Antigenic Liposomes for Generation of Disease-specific Antibodies

Published on: October 25, 2018

Inferring B cell specificity for vaccines using a Bayesian mixture model.

Anna Fowler¹, Jacob D Galson², Johannes Trück²

¹Department of Biostatistics, University of Liverpool, Liverpool, UK. a.fowler@liverpool.ac.uk.

|February 24, 2020

Summary

This summary is machine-generated.

A new Bayesian model can identify vaccine-specific B cell receptors (BCRs) with 69% sensitivity. This statistical approach offers a more detailed analysis of vaccine response than traditional antibody measurements.

Keywords:

B cell receptor High-throughput sequencing Immune repertoire Vaccination

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Detection and Enrichment of Rare Antigen-specific B Cells for Analysis of Phenotype and Function

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Published on: February 16, 2017

Area of Science:

Immunology
Computational Biology
Vaccinology

Background:

Vaccines significantly reduce infectious disease burden, with B cells and antibody production being key to immune response.
Current methods for evaluating vaccine response, like serum antibody analysis, have limitations.
Investigating the B cell receptor (BCR) repertoire offers deeper insights into vaccine-induced immunity.

Purpose of the Study:

To develop a novel Bayesian model for analyzing B cell receptor (BCR) sequences.
To identify vaccine-specific BCRs by analyzing sequence distribution and sharing patterns.
To enhance the evaluation of vaccine response through detailed BCR repertoire analysis.

Main Methods:

Development of a novel Bayesian statistical model.
Analysis of BCR sequence data from two independent studies.
Estimation of model sensitivity for identifying vaccine-specific BCRs.

Main Results:

The Bayesian model effectively describes BCR sequence distributions and sharing patterns.
Vaccine-specific BCRs were identified with an estimated sensitivity of 69%.
Identified vaccine-specific B cells exhibited higher-than-expected sequence similarity.

Conclusions:

Statistical modeling can successfully capture vaccine response patterns and identify specific B cells.
The findings suggest potential for improved vaccine response identification by incorporating additional signals.
This approach provides a more detailed understanding of B cell dynamics post-vaccination.